According to Lynne Rudder Baker’s Practical Realism, we know that we have beliefs, desires, and other propositional attitudes independent of any scientific investigation. Propositional attitudes are an indispensable part of our everyday conception of the world and not in need of scientific validation. This paper asks what is the nature of the attitudes such that we may know them so well from a commonsense perspective. I argue for a self-ascriptivist view, on which we have propositional attitudes in virtue of ascribing (...) them to ourselves. On this view, propositional attitudes are derived representational states, deriving their contents and their attitude types from our self-ascriptions. (shrink)

There is considerable evidence that the hippocampal system contributes both to (1) the temporary maintenance of memories and to (2) the processing of a particular type of memory representation. The findings on amnesia suggest that these two distinguishing features of hippocampal memory processing are orthogonal. Together with anatomical and physiological data, the neuropsychological findings support a model of cortico-hippocampal interactions in which the temporal and representational properties of hippocampal memory processing are mediated separately. We propose that neocortical association areas maintain (...) shortterm memories for specific items and events prior to hippocampal processing as well as providing the final repositories of long-term memory. The parahippocampal region supports intermediate-term storage of individual items, and the hippocampal formation itself mediates an organization of memories according to relevant relationships among items. Hippocampal-cortical interactions produce (i) strong and persistent memories for events, including their constituent elements and the relationships among them, and (ii) a capacity to express memories flexibly across a wide range of circumstances. (shrink)

This paper is a commentary on the target article by Lokendra Shastri & Venkat Ajjanagadde [S&A]: “From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony” in same issue of the journal, pp.417–451. -/- It puts S&A's temporal-synchrony binding method in a broader context, comments on notions of pointing and other ways of associating information - in both computers and connectionist systems - and mentions types of reasoning that are a challenge to (...) S&A's system (amongst many other connectionist systems, recognizing that S&A's is much more capable as regards reasoning than most other contemporary systems). (shrink)

This dissertation argues that mental representation is identical to phenomenal consciousness, and everything else that appears to be both mental and a matter of representation is not genuine mental representation, but either in some way derived from mental representation, or a case of non-mental representation.

Processes comparable in important respects to those underlying human conscious and non-conscious processing can be identified in a range of species and it is argued that these reflect evolutionary precursors of the human processes. A distinction is drawn between two types of processing: stimulus-based and higher-order. For ‘higher-order,’ in humans the operations of processing are themselves associated with conscious awareness. Conscious awareness sets the context for stimulus-based processing and its end-point is accessible to conscious awareness. However, the mechanics of the (...) translation between stimulus and response proceeds without conscious control. The paper argues that higher-order processing is an evolutionary addition to stimulus-based processing. The model’s value is shown for gaining insight into a range of phenomena and their link with consciousness. These include brain damage, learning, memory, development, vision, emotion, motor control, reasoning, the voluntary versus involuntary debate, and mental disorder. (shrink)

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large (...) body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n-ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization. (shrink)

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...) the representation of complex concepts. (shrink)

This paper presents a general computational treatment of how mammals are able to deal with visual objects and environments. The model tries to cover the entire range from behavior and phenomenological experience to detailed neural encodings in crude but computationally plausible reductive steps. The problems addressed include perceptual constancies, eye movements and the stable visual world, object descriptions, perceptual generalizations, and the representation of extrapersonal space.The entire development is based on an action-oriented notion of perception. The observer is assumed to (...) be continuously sampling the ambient light for information of current value. The central problem of vision is taken to be categorizing and locating objects in the environment. The critical step in this process is the linking of visual information to symbolic object descriptions; this is calledindexing, from the analogy of identifying a book from index terms. The system must also identifysituationsand use this knowledge to guide movement and other actions in the environment. The treatment focuses on the different representations of information used in the visual system.The four representational frames capture information in the following forms: retinotopic, head-based, symbolic, and allocentric. The functional roles of the four frames, the communication among them, and their suggested neurophysiological realization constitute the core of the paper. The model is perforce crude, but appears to be consistent with all relevant findings. (shrink)